Run UMAP dimension reduction

run UMAP on subset and project on the rest

runUMAPprojection(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  dim_reduction_to_use = "pca",
  dim_reduction_name = NULL,
  dimensions_to_use = 1:10,
  random_subset = 500,
  name = NULL,
  feats_to_use = NULL,
  return_gobject = TRUE,
  n_neighbors = 40,
  n_components = 2,
  n_epochs = 400,
  min_dist = 0.01,
  n_threads = NA,
  spread = 5,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = TRUE,
  toplevel_params = 2,
  ...
)

Arguments

gobject

giotto object

feat_type

feature type

spat_unit

spatial unit

expression_values

expression values to use

reduction

'cells' or 'feats'

dim_reduction_to_use

use another dimension reduction set as input

dim_reduction_name

name of dimension reduction set to use

dimensions_to_use

number of dimensions to use as input

random_subset

random subset to perform UMAP on

name

arbitrary name for UMAP run

feats_to_use

if dim_reduction_to_use = NULL, which features to use

return_gobject

boolean: return giotto object (default = TRUE)

n_neighbors

UMAP param: number of neighbors

n_components

UMAP param: number of components

n_epochs

UMAP param: number of epochs

min_dist

UMAP param: minimum distance

n_threads

UMAP param: threads/cores to use

spread

UMAP param: spread

set_seed

use of seed

seed_number

seed number to use

verbose

verbosity of function

toplevel_params

parameters to extract

...

additional UMAP parameters

Value

giotto object with updated UMAP dimension reduction

Details

See umap for more information about these and other parameters.

• Input for UMAP dimension reduction can be another dimension reduction (default = 'pca')

• To use gene expression as input set dim_reduction_to_use = NULL

• If dim_reduction_to_use = NULL, feats_to_use can be used to select a column name of highly variable genes (see calculateHVF) or simply provide a vector of genes

• multiple UMAP results can be stored by changing the name of the analysis

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> 
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

runUMAPprojection(g)
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap umap.projection tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used